Author(s): Red Peruana Ciclo de Vida
Download publication (654.03 KB)Author(s): Red Peruana Ciclo de Vida
Download publication (2.13 MB)Author(s): Luis Izquierdo Horna, Ramzy Kahhat Abedrabbo, Ian Vázquez Rowe
(Español) Accurately forecasting municipal solid waste (MSW) generation is essential for designing efficient waste management systems and promoting sustainable urban development. As cities expand and consumption patterns shift, reliable data-driven approaches are increasingly necessary to address the complexities of MSW generation. This study applied the random forest (RF) algorithm, a machine learning technique, to predict MSW generation at the household level. RF was selected for its capacity to handle non-linear relationships, imbalanced datasets, and outliers. The analysis focused on data from 2019, avoiding distortions associated with the COVID-19 pandemic. The model integrated per capita MSW data with household fuel consumption indicators (i.e., natural gas, electricity, and liquefied petroleum gas) and demographic variables such as age, education level, and monthly expenditure. The case study focused on the city of Lima, Peru, using 80 % of the data for training and 20 % for testing, with hyperparameters optimized via 5-fold cross-validation. The final model explained 55 % of the variance in MSW generation (R² = 0.55). This result reflects the model’s ability to capture significant drivers of variability, although it leaves room for refinement due to factors not included in the analysis, such as cultural practices or seasonality. Among the predictors, household monthly expenditure on cooking fuels emerged as the most influential variable, reinforcing the connection between resource consumption and waste generation. These findings highlight the potential of integrating socioeconomic indicators into predictive models to enhance their reliability. By improving forecasting capabilities, this study supports targeted policies for urban waste management and sustainable resource use.
Open linkAuthor(s): Red Peruana Ciclo de Vida y (Español) Alejandro Gallego-Schmid, Ana Belén Guerrero, Ricardo Rebolledo-Leiva, Alvaro Elorrieta-Mendoza, Denisse Milagros Paredes Cotohuanca, Raphael Ricardo Zepon Tarpani, Rodrigo Salvador, Murillo Vetroni Barros, Claudia E. Henninger, Leonardo Vásquez-Ibarra
(Español) A well-functioning circular economy (CE) integrates resilience across economic, environmental, and social dimensions. This study identifies key drivers and barriers to Brazil's CE transition through 20 semi-structured interviews with stakeholders. Major sociocultural barriers include inadequate education and limited CE awareness, while growing environmental consciousness and traditional reuse practices act as drivers. Policy barriers stem from the absence of dedicated CE regulations, though national sustainability efforts offer opportunities. Technological limitations arise from insufficient research, but efficiency-enhancing innovations and digital business models show promise. Economically, high transition costs hinder progress, whereas resource efficiency boosts competitiveness and job creation. Key priorities for advancing CE include raising public awareness, integrating CE into education, supporting waste pickers, developing decentralised regulations, improving waste management, fostering innovation hubs, and providing financial incentives for circular business models. Stakeholder engagement—particularly policy-makers, civil society, and private enterprises—remains essential to accelerating CE adoption in Brazil.
